Study On The Detection And Tracking Algorithm Of Moving Vesicles In Living Cells And The Method Of Single Molecule Fluorescence Resonance Energy Transfer

With the rapid development of microscopy imaging technology,it is easy to acquire time-lapse images of biological macromolecules' dynamic process by fluorescent labeling them.However,how to deal with these long time image sequences is difficult and hot.Biologists often analyze fluorescence images manually.This process is time-consuming and laborious.In addition,manually analysis will produce subjective bias.In other words,the results of the analysis are largely dependent on individual skills,judgments and preferences.Therefore this paper focuses on studying biological image processing algorithms,and develops automatical analysis to handle these complex data and to fully exploit them for describing biological process on a quantitative level and building accurate mathematical models of dynamic structure to get some new biology findings.In the first part of this paper,we studied the detection and tracking algorithms of moving vesicles in living cells.In the aspect of vesicle detection,we try to apply adaptive threshold algorithm,local maximum algorithm,wavelet transform algorithm,watershed algorithm,compressive sensing algorithm and machine learning algorithm to living cell for vesicle detection.After comparing and analysis,we found that the wavelet transform method is able to detect the largest number of vesicles,and the number of correct detection can be further increased by adding watershed algorithm.Meanwhile,a"self-checking" algorithm was proposed to correct the error of other algorithm in order to improve the detection accuracy further.The main idea of this algorithm is to construct a multi-kernel function superposition model and use the model to fit the data at the indistinguishable moment;the number of vesicles and the central positions of vesicles are determined from the set based on ?2-statistics of the residuals in least-square fits of the models to the image data.In the aspect of vesicle tracking algorithm,we studied the multiple hypothesis tracking algorithm,Kalman filtering algorithm and interactive multiple model algorithm,and proposed an optimized flow chart of vesicle tracking.We analyzed the movement track of the vesicles in ? cell using optimized tracking algorithm before and after glucose stimulation.We found that the number of vesicle traces increased and the average docking time of vesicles decreased after glucose stimulation based on our tracking analysis.This is because ? cells will release insulin to regulate glucose balance with the help of vesicle translocation and secretion after glucose stimulation.Meanwhile we analyzed the docking time of WT cell and KO cell after stimulation.We found that the average docking time of Spire1 KO cell reduced less than half of WT cell,which indicated that Spire1 may be involved in vesicle docking,and play a role to stabilize the vesicle docking.In a word,we quantified the vesicles activity in mice ? cell by tracking analysis on subcellular level.In the second part of this paper,we focused on the application of single molecule fluorescence resonance energy transfer(smFRET)in biology.In this part,we analyzed the performance of wavelet transform algorithm and rolling ball algorithm in background noise removal and signal extraction from single molecule images,and proposed the flow chart of automatic processing of single molecule FRET.Meanwhile,we calculated the statistical histogram of 15 bp DNA FRET efficiency.By Gaussian fitting,we obtained the result that 15 bp DNA FRET efficiency was approximately 0.634 and the corresponding distance was about 5.47nm.The discrepancy between the real distance and calculated distance was 0.37nm,which may be due to the orientation of the labeled dyes.In general,these two algorithms can be applied to the single molecule FRET images to improve processing efficiency.Moreover,we also calculated the statistical histogram of Syntaxinl FRET efficiency.By Gaussian fitting,we found that Syntaxinl has two states.One of them may be "on" state which has a larger distance,and the other is "closed" state which has a smaller distance.In the third part of this paper,we focus on the evaluation of autofocus algorithms for automatic detection of Caenorhabditis elegans lipid droplets.In this paper,We evaluated 16 autofocus algorithms which were collected form well-known algorithms as well as the most recently proposed focusing algorithms to found the algorithm which is best for C.elegans lipid droplets images.In our study,WT show a poor accuracy,and has a long computational time,so it is not suitable for C.elegans lipid droplets,although WT may have a good performance in other applications.Comprehensive consideration of accuracy and computational time,we recommend ATEN,MDCT and TEN for C.elegans lipid droplets.Moreover,ATEN achieves the best accuracy.In an automatic screening system,we often require both high accuracy and fast acquisition.In this case,we can apply the fastest algorithm TH for rough search,and then apply ATEN algorithm for fine search.